Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud?
Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems?
Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment?
If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day.
Key job responsibilities
• Demonstrate proficiency in supervised algorithms (tree-based models, neural networks) and unsupervised algorithms (clustering).
• Rapidly design, prototype, and test multiple hypotheses in a high-ambiguity environment, utilizing quantitative analysis and business judgment.
• Report findings in a scientifically rigorous manner.
• Collaborate with software engineers, product managers, and domain experts to identify and address challenges requiring innovative solutions for our products.
• Acquire knowledge of Amazon's diverse data resources and determine when, how, and which resources to leverage or exclude.
• Integrate successful experiments into large-scale, highly complex production services by collaborating with software engineering teams.
• Maintain technical documentation and effectively communicate results to diverse audiences.
• Evaluate trade-offs by considering complexity, long-term benefits, and the reusability of existing solution
- - 7+ years of data scientist experience or similar role
- - 7+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- - Experience with statistical models e.g. multinomial logistic regression
- - Bachelor's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- - Experience with statistical models e.g. multinomial logistic regression
- - Proven knowledge of deep learning and experience hosting and deploying ML solutions (e.g., for training, tuning, and inferences)
- - Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue)
- - Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
- - Experience as a technical leader and mentor on a data science team